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import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# Download and load the trained model
model_path = hf_hub_download(repo_id="pal14/visitWithUs_classification-model", filename="best_classification_model_visitWithUs.joblib")
model = joblib.load(model_path)
# Streamlit UI
st.title("Tourism Package Prediction")
st.write("""
This application predicts predicts whether a customer will purchase the newly introduced Wellness Tourism Package before contacting them.
Please enter the app details below to get a prediction.
""")
# User input
Age = st.number_input("Age", min_value=0.0, max_value=100.0)
TypeofContact = st.selectbox("Contact Type", ["Self Enquiry", "Company Invited"])
CityTier = st.selectbox("City Tier", [1, 2, 3])
Occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"])
Gender = st.selectbox("Gender", ["Male", "Female"])
NumberOfPersonVisiting = st.number_input("Number of Person Visiting", min_value=0, max_value=100000000)
PreferredPropertyStar = st.selectbox("Preferred Property Star", [3., 4., 5.])
MaritalStatus = st.selectbox("Marital Status", ["Single", "Divorced", "Married", "Unmarried"])
NumberOfTrips = st.number_input("Number of Trips", min_value=0.0, max_value=10000.0)
Passport = st.selectbox("Passport", [0, 1])
OwnCar = st.selectbox("Own Car", [0, 1])
NumberOfChildrenVisiting = st.selectbox("Number of children visiting", [0., 1., 2., 3.])
Designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP", "VP"])
MonthlyIncome = st.number_input("Monthly Income", min_value=0.0, max_value=1000000.)
# Assemble input into DataFrame
input_data = pd.DataFrame([{
'Age': Age,
'TypeofContact': TypeofContact,
'CityTier': CityTier,
'Occupation': Occupation,
'Gender': Gender,
'NumberOfPersonVisiting': NumberOfPersonVisiting,
'PreferredPropertyStar': PreferredPropertyStar,
'MaritalStatus': MaritalStatus,
'NumberOfTrips': NumberOfTrips,
'Passport': Passport,
'OwnCar': OwnCar,
'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
'Designation': Designation,
'MonthlyIncome': MonthlyIncome,
}])
# Predict button
if st.button("Predict"):
prediction = model.predict(input_data)[0]
st.subheader("Prediction Result:")
st.success(f"Package Taken: {prediction}")
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